With the growing concern over sustainable energy in Pakistan is very critical so this paper presents the performance of Tetrafluoroethane (R134a) and Isobutane(R600a). Refrigerants are evaluated under specific environmental and desired space temperatures using the experimentally and numerically through MATLAB Simulation. As a limitation of this work, the MATLAB-based simulation analyses the Coefficient of Performance (COP), cooling load, and power input for a vapor-compression refrigeration system. In this study, the Numerically/Experimentally analyse the thermodynamic data, including enthalpy values based on ASHRAE standards, was utilized to predict refrigerating effect and compressor work across a range of evaporator temperatures (−5°C, 10°C, 15°C, 20°C) and room temperatures (35°C to 55°C), for the specific region of Multan, Pakistan. Several Studies have investigated this, but my outcomes are compared to prior literature, including Aized et al. (2022) and Sánchez et al. (2022), to validate findings and provide context for the suitability of low-GWP alternatives. The numerical and experimental results show excellent agreement, with the percentage error data approximately 1- 1.8% for both the calculation of COP and COST of Electricity. The findings of this research elaborate that isobutane (R600a) is outperform as compared the tetrafluroethane(R134a) for the hot climate of Multan, Pakistan, and transitioning to sustainable refrigerant solutions.
This research aims to develop and implement a hybrid power generation system that includes solar PV panels and a household-scale pumped mini-hydropower system independent of natural river flow to provide a potential off-grid electricity supply to households in Nigeria. The research methodology employed for this study involves simulations using HOMER Pro software to understand the functionality of a system comprising a 2.40 kWp PV array and a 1 kW mini-hydro unit driven by pumped gravitational energy storage using domestic water infrastructure. In addition to technical performance evaluation, an economic assessment was conducted using Net Present Cost (NPC) and Levelized Cost of Electricity (LCOE) metrics to substantiate system. The main insights drawn are that the hybrid system can supply the electricity demand for an average household and its operations throughout the year achieve 100% renewable, battery-less operation and zero unmet load complemented by reliable backup power covering periods of low solar irradiation. The analysis incorporates location-specific solar irradiance data and realistic Nigerian residential load profiles, enhancing real-world applicability. Additionally, the study provides new techno-economic benchmarks (NPC and LCOE) for fully renewable residential PV-pumped – hydro systems in sub-Saharan Africa. The policy implications of these are that adoption of hybrid renewable systems can improve energy security while lowering reliance on the grid, and achieve greater ecological balance in the country. This research advocates for policies that provide financial support for investment in renewable energy technology and infrastructure, alongside grants to homeowners for the adoption of energy-efficient systems. To motivate the homeowners and stimulate investment in these renewable energy solutions, this study proposes financing policy and renewable energy technology infrastructure which are investments worth pursuing.
In this paper, a comparative and simulation-based analysis of advanced 3D printing materials and additive manufacturing methods has been made with Thermoplastic Polyurethane (TPU), carbon fiber composites, and metal powders with Fused Deposition Modeling (FDM), Electron Beam Melting (EBM), Continuous Fiber Fabrication (CFF), and Directed Energy Deposition (DED) methods. It suggests a material-focused adaptive optimization system, which incorporates monomer personalization, machine learning-based forecasting, and real-time environmental regulation. The findings indicate quantifiable gains, durability of around 75, thermal resistance of 80-85 and chemical resistance of 79-87, being better than the traditional methods. Moreover, the given approach is cost effective (circa 0.85) in contrast to the smaller figures in metal-based procedures like EBM and DED. The results indicate improved structural performance, stability, and design flexibility and the process is fit in aerospace, automotive and healthcare applications where high performance and flexibility are paramount.
This study, with a primary focus on mitigation of losses, has been carried out by exploring complex dynamics of optimal operation of multiple distributed generations within a distributed system. For this work, actual load data from New Satellite Town, Sultanpur region, is used. Exploring load data has shown a significant voltage drop in overload and unbalanced operating conditions. A simulation framework using ETAP software was developed to mitigate these losses. A digital twin to mimic a real-world scenario, in this framework, components of the power distribution network, including transmission lines, transformers, and various loads, are integrated. A comprehensive investigation into the effectiveness of capacitors in addressing overload and imbalance problems is carried out. The best capacitor capacity (kVA) is carefully identified through this investigation. To provide reliability and improved efficiency in those areas with loss of voltage, PV systems will be installed as a distributed generator to support increased electricity production for load balancing purposes. This research has developed an approach to promote the use of renewable energy resources to increase the capacity of an electrical distribution system. The goal is to improve energy sustainability through the identification of potential improvements and the reduction of losses.
The fast development of digital learning technologies has re-imagined the interaction between students and educational material; however, there are still numerous platforms that are struggling with poor engagement and inability to perform a seamless personalization. EduAssist is proposed as an adaptive learning tool, providing gamified educational data to enhance the performance of students and their motivation. The system integrates customized testing, feedback loops, and gamification to allow learner-improving and interactive learning experience. EduAssist pinpoints the area of weakness of learners using adaptive assessment and performance analytics and constructs specific recommendations with the help of AI-based feedback. The system ensures motivation by employing gamification elements (XP points, streak tracking, and achievement badges) that you get upon progress and can aid to build the habit of learning. Developed on the basis of contemporary web architecture based on Next.js, NestJS, and MongoDB, EduAssist is scaled, responsive, and provides safe data processing. The outcomes of Pilot evaluation and prototype testing show that individualized feedback and adaptive learning flows contribute to better engagement and perceived skill development among the learners. The platform demonstrates how academic learning environments can address the motivation-mastery gap through the use of personalization and data-driven gamification.
Unmanned Aerial Vehicles (UAVs) are transforming modern logistics by enabling autonomous systems. However, safe navigation in urban and rural environments is still a challenge due to obstacles such as trees, buildings, houses and transmission wires. This research presents the development of an autonomous drone from point-to-point navigation, integrating real time obstacle avoidance. The system integrates a Pixhawk 6x as the flight controller and a Jetson Nano as the onboard processing unit. A camera and a Lidar sensor take the input from the real environment, enabling obstacle detection using a deep learning model (Yolov8) and Convolutional Neural Network (CNN). The Jetson Nano processes real time video streams, detects obstacles and sends corrective navigation commands to the Pixhawk flight controller using the MAV Link protocol. Based on the detected obstacles, the drone adjusts its trajectory. It ensures a safe and efficient flight path between pre-defined waypoints. The proposed system is tested in various environments. It shows good obstacle detection and real time flight path correction. The system is tested experimentally and the result shows an improvement in (1) real-time object detection accuracy, (2) obstacle avoidance efficiency and (3) navigation stability. Along with avoiding the obstacles, path correction algorithms reroute around the obstacles with minimal deviation from its original course. This research contributes to advancing autonomous UAV navigation systems, particularly in obstacle avoidance. Our findings show the potential for UAVs to work safely and autonomously in real world conditions. This work can be expanded to integrate LiDAR for enhanced depth sensitivity. It can expand the system’s capabilities for multi-object tracking and decision making in complex environments.
This research paper investigates the stator skewing effect on the no-load characteristics of the V-shaped interior permanent magnet (IPM) motor for hybrid electric vehicle (HEV) traction application. A multi-sliced 2D finite element analysis (FEA) method is adopted to analyze the skewed-stator V-shaped IPM motor, at no-load conditions without stator current excitation. The multi-sliced 2D FEA consumes less amount of the computational time compared to a traditional 3D FEA for the skewed-stator V-shaped IPM motor. To validate the computational accuracy of the multi-sliced 2D FEA method, the cogging torque of the non-skewed V-shaped IPM motor is compared with the 2D FEA. The no-load characteristics, such as cogging torque and back EMF of the V-shaped IPM motor with and without stator skewing are computed using the multi-sliced 2D FEA, and the comparative characteristic analysis is carried out.
This work examines the experimental method of energy harvesting with a speed breaker based on piezoelectric materials, specifically lead zirconate titanate (PZT). The system transforms the mechanical stress of the vehicles into electrical power to drive the streetlights and other transport-related electronics. In SolidWorks, a CAD model was created that included piezoelectric disks, copper electrodes, a piston-cylinder configuration, and springs, after which structural analysis was done in ANSYS to determine the stress, strain, and deformation of the design under car and motorcycle load. A three-disk piezoelectric prototype was produced, and a series of three piezoelectric disks was connected, and a rectifier circuit was added. Motorcycles and cars were used to test voltage generation at 0 and 15 km/h. Findings indicated that the weight of the vehicle and speed have a significant effect on output voltage; light motorcycles yielded a maximum of 4.13 V, whereas heavy cars yielded a maximum of 14.16 V. The paper shows that road traffic is an effective way of generating a renewable energy source that can sustain cleaner and more efficient urban power systems.
UML (Unified Modeling Language) is a foundational tool needed for teaching software engineering. Despite this, when taught with the traditional approach, students typically find it difficult to grasp the syntax, semantics, and diagramming techniques used in UML. This paper proposes a new approach to teaching UML through an AI-enhanced gamified learning platform, which will increase student motivation, improve understanding of UML concepts, and facilitate the development of practical UML modelling skills. The proposed AI-enhanced gamified learning platform for UML includes various gamification components (e.g., XP progression, achievement badges, streaks, leaderboards, and scenario-based missions), as well as an AI-assisted diagram evaluation system and an adaptive learning system. To create a comprehensive learning experience, the platform includes (1) the use of theoretical quizzes and (2) the completion of practical diagram building assignments, an automated error detection tool, and a spaced repetition flash card system. A quantitative analysis of this gamified learning platform is conducted using a structured Likert-type questionnaire and statistical tools (IBM SPSS Statistics) on undergraduate software engineering students and Computer Science students. Quantitative results suggest that AI-assisted learning feedback and gamification have a statistically significant effect on learning effectiveness, engagement, UML skill development, and overall learner satisfaction. The results of this study provide evidence that the integration of AI-based feedback and gamified educational delivery methods can positively impact the teaching of software engineering and provide a scalable solution for delivering UML education.
A rapid rise in DC faults is observed due to the unavailability of properly designed protection schemes for DC networks. The most prevalent faults are line-to-line L-L and line-ground L-G, etc. For modern multi-terminal high voltage systems MTHVS, DC fault protection is a top priority. The fault detection speed for HVDC systems must be very fast due to the rapid rise in fault current. For conventional low HP drives, undervoltage protection is based on the measured voltage on the DC bus of the drive’s unit, rather than the AC input voltage. This fact must be understood to help with troubleshooting. The need for additional power is growing to satisfy the increasing load demand, making High Voltage Direct Current (HVDC) transmissions important. NTDC suffered transmission and distribution losses totaling about 18%. The energy shortages can be mitigated by increasing the usage of renewable energy sources and decreasing losses. HVDC power transmission may be essential for Pakistan's electricity sector to meet these objectives. In this research work, the focus is to create primary and secondary HVDC protection based on a relaying algorithm. Numerous projects and novel designs have been developed concerning these specific relay logic schemes; however, the goal of this scheme is to produce a new PLC-based relay logic technique.
This research paper deals with the electromagnetic analysis of the V-shaped interior permanent magnet (IPM) motor for the hybrid electric vehicle (HEV). Like Toyota prius III traction motor, a V-typed single-layered IPM motor is investigated. A two-dimensional (2D) finite element analysis (FEA) methodology is discussed and adopted to compute the electromagnetic characteristics of the IPM motor, at no load conditions, with and without stator current excitation, and accelerating from starting to constant speed conditions. The cogging torque of the IPM motor at no load and zero stator current is calculated to ascertain the dynamic stability as well as instability positions of the motor that is of great essence in understanding the startup behavior and the overall performance of the motor. An existing control method modulates the stator supply current, depending on the position of the rotor of the IPM motor. This control approach is used to provide a fixed phase difference between the rotor and the stator magnetic fields so that the IPM motor can smoothly accelerate as it goes into the constant speed. The electromagnetic torque oscillates during the IPM motor's acceleration and despite of the fluctuations, the V-shaped IPM motor demonstrates the reliable performance, and accelerating and stabilizing at its target speed. The 2D FEA is utilized to investigate the IPM motor's dynamic behavior during the startup for its potential application in hybrid electric vehicle (HEV) traction systems.
This research study looks at using the cutting-edge object detection algorithm YOLOv8 to detect potholes on a road surface. Potholes make roads and infrastructure unsafe; thus, effective and precise ways of detecting potholes are required to act on them in time. The paper focuses on the optimization of the YOLOv8 parameters to address better pothole detection capability of the model in diverse environmental conditions. A dataset of potholes created is used to train YOLOv8. The photos that are used to build the dataset are composed of those of various cities in Pakistan. When using the model, many variables of the model, such as Batch size, Epochs, learning rate, and optimizer, are systematically investigated and optimized. The outcome received of the 32 parameters of the batch size is 0.87% precision, 0.82% recall and 0.88% detection accuracy. Regarding epochs, the model achieved 0.87 precision, 0.84 recall, and 0.89 detection accuracy in 200 epochs. The model parameter test learning rate was performed at four more learning rates. These findings indicate that the learning rate of 0.001 was the best to achieve the best performance of our model to a maximum 0.85 precision, 0.82 and 0.88 recall and detection accuracy, respectively, with respect to optimizer. The findings of the experiment indicated that AdamW was more successful than the other optimizers to train the model in precision, recall, and accuracy. As noted in the research, parameter adjustment is important to optimize the performance of YOLOv8 when used to detect potholes.
The shell and tube heat exchangers are extensively used in the different industrial processes and conventional designs mostly suffer from higher manufacturing costs and lower thermal efficiency. This study presents the fabrication, design and industry level performance evaluation for a cost-effective and compact shell and tube heat exchanger using a spiral rod for enhancement of heat transfer. The experiment was performed on a unit, developed from mild steel components and copper valves providing higher affordability and durability. The experimental trials were conducted in real-time industrial conditions by using chilled and shower as working fluids under a configuration with performance estimated using thermal effectiveness, heat transfer rate, Reynolds number, log mean temperature difference (LMTD). The addition of spiral rod resulted in significant turbulence, quantitatively improved the heat transfer rate from 7.63kW to 9.01kW, improved effectiveness from 51% to 84%, and provided significant improvement in thermal performance. Moreover, the cost analysis indicated a fabrication cost of approximately 38700PKR/=, representing 25%-30% saving when compared to conventional heat exchangers.
Over the last decade, crams on transformer oil-based nanofluids has advanced rapidly. Compared to pure transformer oils, vegetable oil-based nanofluids may be considered the future insulation fluids since they offer a unique potential for improving breakdown strength and heat transmission efficiency. In this paper, nanofluids (NFs) were made by dispersing nanoparticles (NPs) into mustard oil as the base oil because of their superior electrical and thermal properties. To create nanofluids, two types of nanoparticles, insulating nanoparticles Al2O3 and SiO2, were chosen and suspended in mustard oil at varying concentrations of 0.15 g/L, 0.3 g/L and 0.5g/L. The breakdown strength of AC and DC in oil samples with and without nanoparticles was determined using IEC 60156 standard test with a gap of 2.5 mm and a voltage rise rate of 2.5 kV/s. Summarizes the experiment's findings for the Alumina and silica-based nanofluids and their concentrations. The enhancement in breakdown voltage was approximately 15.23%, increased by adding alumina 0.15g/L concentration. It was also observed that the improvement in breakdown voltage was about 11.51% by adding silica 0.5g/L concentration. The experimental results were compared to those from earlier studies, revealing that most of the test results obtained in this study were comparable to those obtained in previous studies.
Wi-Fi fingerprinting is one of the cheapest and easiest methods to find your way about indoors because it works with a variety of different types of infrastructure. But problems like signal fluctuation, noise from the environment, and different types of devices still make positioning less accurate. In this study, we propose a deep learning framework that combines a Transformer encoder with an Autoencoder-based feature extractor to solve these problems. The Transformer module captures long-range dependencies and structural patterns across high-dimensional RSS vectors, while the Autoencoder compresses features and reduces noise in a strong way. Our architecture optimizes representation learning and sequence modelling together from start to finish, which is different from how traditional deep neural networks work. Tests on a UJIIndoorLoc Wi-Fi fingerprint dataset show that our method does much better than a baseline deep neural network in terms of both classification accuracy and mean localisation error. These results show that the Transformer-Autoencoder framework works well to make indoor positioning systems more accurate and reliable.
Software Architecture provides views and structure of the system at a high-level of abstraction. The selection of software architecture and user interface design has a great influence upon the quality of software. Our research is going in two directions; one to find the impact of layered architectural style upon maintainability of software and secondly to find the impact of user interface design with three-dimensional (3D) modeling upon usability of software. We need to choose architectural solutions based on the proven facts in order to find the desired quality attributes. The literature for the impact of architecture patterns on quality attributes shows a lack in achieving usability, that’s why we have chosen to assess a software architectural pattern along with interface design in order to achieve usability along with other quality attributes. We implemented a system “Electronic Communication Surveillance Analytic tool with 3D Modeling” based on the layered architectural style, then we evaluated this system for maintainability and usability.
In recent years, the use of electrical energy has significantly increased. To meet the growing demand, new generating units have been installed across the country, accompanied by the establishment of extensive transmission networks to deliver this power to the grid, which supplies various loads. Additionally, grids have been interconnected to enhance the reliability and availability of electrical power. In an interconnected electrical network, uneven loading can affect total power transmission from generation to consumer being lower than expected, which results in the utilization of transmission lines is not optimal. To manage energy congestion and enhance voltage stability, it is crucial to have control over power flow. This research introduces a technique to manage power flow using a Phase Shifting Transformer (PST) to boost the overall transfer capacity of the transmission lines and avoid overloading in the grid. The PST controls power flow by adjusting the phase angle of the voltage system. The study includes a MATLAB Simulink model of a power system, implementing PST for power flow control in multiple power plants. It also delves into enhancements in the system's response to short-term changes and investigates the impact of varying loads. The results indicate that PSTs can effectively optimize power distribution, diminishing the likelihood of line overload. Furthermore, the model demonstrates improved system stability and dependability across different load scenarios.
The rise of ride-share services and platforms has significantly transformed urban mobility and solved many transportation challenges. However, there is no dedicated ride-sharing service specifically designed to address the needs of students in educational institutions. To bridge this gap, we introduce RidePool, a dynamic ride-sharing application. RidePool has been developed using an agile development process, utilizing technologies including Node.js, React Native, MongoDB, and other APIs for payment integration and location management. Socket communication, ride management, location management, file handling, user module, and a feedback system make up the application's six main components. RidePool attempts to fill a major void in the transportation landscape, allowing students, faculty, and staff to commute in a more sustainable, economical, and socially inclusive manner. To guarantee user safety and dependability, the application integrates advanced features such as secure Microsoft user authentication, a robust rating system for better ride selection, real-time tracking, and secure payment alternatives. The proposed solution is intended to link students with comparable schedules and locations, providing a cost-effective, convenient, and eco-friendly substitute for the existing services. The application addresses the shortcomings of current ride-sharing services and meets the unique needs of the Pakistani university community.
The performance of the flexible pavement is based on the characterization of hot mix asphalt (HMA). The alterations in climatic conditions and traffic loading influence the rheological properties of asphalt. The asphalt viscosities are an important parameter of asphalt that gives resistance to permanent deformation and stability in the various application areas. Polymer modification can be used to enhance Viscoelastic properties of asphalt binder to enhance its resistance against rutting and cracking. This study developed polymer-modified asphalt samples and tested them using a dynamic shear rheometer at seven temperatures (46, 52, 58, 64, 70, 76, and 82 oC) for frequency sweep (1 to 100 rad/s). Master curves were developed by choosing 64 oC as a reference temperature and applying shift factors. The results depicted that the polymer modification has a substantial influence on enhancing the elastic behavior of asphalt. Attock PMA sample is stiffer compared to laboratory polymer-modified asphalt (PMA) samples, which is determined by the high zero shear viscosity. The behavior of PMA samples at 1.7% and 2.0% of polymer content is similar, which indicates that 1.7% of polymer content can be used as the optimum value for polymer modification. A 2nd-degree polynomial model has the best fit for the master curve of complex viscosity with R2 = 0.921. The values of ZSV indicated that the PMA samples are resistant to permanent deformation, because it increases the stiffness and elasticity of the samples. These findings involve substantial information as to the practical use of polymer-modified asphalt and the way it can be applied in the improvement of pavement performance, durability and offer valuable information on how to select materials to use in designing an asphalt mix.
The seasonal rain in Bangladesh has been very important in supporting the agricultural sector of the country, but because of climate change, it has become more erratic. In this research, the authors used historical meteorological data, 2013 to 2022, to create and test four machine learning algorithms, AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) to predict rainfall with sufficient accuracy. Random Forest proved to be the best-performing model having the highest accuracy, precision, and F1-score, and AdaBoost has a high recall, meaning that it is more effective in identifying events of rainfall. The results emphasize the potential of ensemble learning methods to aid smart agriculture to enhance the irrigation scheduling, counteracting weather-related risks, and enhancing food security. The model proposed helps in developing a climate-resilient decision-support model that will help farmers and policy makers make sound decisions in agricultural planning as the rainfall patterns continue to be highly unpredictable.
